Likelihood-free inference refers to inference when a likelihood function
cannot be explicitly evaluated, which is often the case for models based on
simulators. Most of the literature is based on sample-based `Approximate
Bayesian Computation' methods, but recent work suggests that approaches based
on deep neural conditional density estimators can obtain state-of-the-art
results with fewer simulations. The neural approaches vary in how they choose
which simulations to run and what they learn: an approximate posterior or a
surrogate likelihood. This work provides some direct controlled comparisons
between these choices.

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